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Ford uses Google Prediction API for Navigation Systems

| May 13, 2011

11 May 2011 – Ford researchers are harnessing the power of cloud computing, analytics and Google innovation to identify technologies that could make tomorrow’s vehicles smart enough to independently change how they perform to deliver optimal driveability and fuel efficiency.

Ford researchers are applying Google’s Prediction API to more than two years of their own predictive driver behavior research and analysis. The Google API can convert information such as historical driving data – where a driver has traveled and at what time of day for example – into useful real-time predictions, such as where a driver is headed at the time of departure.

How it works

Ford is hoping to use these types of cloud-stored data to enable a vehicle essentially to optimize itself and perform in the best manner determined by a predicted route.

This week, Ford researchers are presenting a conceptual case of how the Google Prediction API could alter the performance of a plug-in hybrid electric vehicle at the 2011 Google I/O developer conference. Here’s how the technology could work:

• After a vehicle owner opts in to use the service, an encrypted driver data usage profile is built based on routes and time of travel. In essence, the system learns key information about how the driver is using the vehicle

• Upon starting the vehicle, Google Prediction will use historical driving behavior to evaluate given the current time of day and location to develop a prediction of the most likely destination and how to optimize driving performance to and from that location

• An on-board computer might say, “Good morning, are you going to work?” If the driver is in fact going to work, the response would be, “Yes,” and then an optimized powertrain control strategy would be created for the trip. A predicted route of travel could include an area restricted to electric-only driving. Therefore, the plug-in hybrid could program itself to optimize energy usage over the total distance of the route in order to preserve enough battery power to switch to all-electric mode when traveling within the EV-only zone

Because of the large amount of computing power necessary to make the predictions and optimizations, an off-board system that connects through the cloud is currently necessary.

Work is now underway to study the feasibility of incorporating other variables such as driver style and habits into the optimization process so Ford can further optimize vehicle control systems, allowing car and driver to work together to maximize energy efficiency.

Read more here.

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